Broadly, I am interested in the flexibility of human thought. Humans are able to think and reason about a whole lot of things: themselves and others, their environment, fictional environments, abstractions (e.g. mathematical systems), etc. They are also able to think about each of these things in a variety of ways: I can think of my car as whole entity, as a system of interconnected parts, or as a unit in a larger system of cars, etc. Further, each of these different ways of thinking are useful in serving different goals: locating the car when I'm searching for where it's parked, fixing the car, driving safely, etc.
A construct used to explain this property of human thought is mental representation. Just as the word "car" is a linguistic entity that is distinct from but that represents a kind of thing - namely, cars - mental representations are entities that are distinct from but that represent things or kinds of things. Mental representations can be thought of as the bodies of knowledge about their referents that are used by default in higher cognitive processes like language use, problem solving, or coming up with explanations (Machery, 2009). To return to the example above, thinking of my car in the different ways mentioned seems to require three different kinds of mental representations: To think of my car as a whole entity seems to involve a concept, to think of my car as a system of interconnected parts seems to involve a relational schema, and to think of my car as a unit in a larger system of cars seems to involve a relational concept. Specifically, I study phenomena like analogical reasoning, metaphor comprehension, and causal reasoning. I think successful explanations of these phenomena are crucial in explaining the flexibility of the human cognitive system.
The BART project examines a particular kind of mental representation useful for representing relations and can be thought of as a basic element of relational schemas. Predictably, mental representations of this kind allow humans to understand various entities as being related to each other in various ways. This ability includes, for example, being able to see that the following entities instantiate the italicized relations: that a spear is a kind of weapon, that gluttony is an excessive form of eating, that the value 10 is 2 plus 8 or 2 more than 8. We are modeling the emergence of these representations and are examining to what extent aspects of our associationist Bayesian model can predict human behavior and neural activity.
A Causal Explanation of Basic-Level Categories (with Patricia Cheng)
Objects that humans encounter in their daily lives can be categorized across various levels of abstraction and along various dimensions. Basic level categories are the most typical answers to the question: “what is that?” For example, when I encounter a dog, I refer to it as a dog, even when I could have also successfully referred to it as a mammal or a golden retriever. Why do human reasoners have the basic-level categories that they do; what privileges dog over mammal and golden retriever?
This project aims to develop and evaluate an explanation for basic level categories that emphasizes causal reasoning. Following C. I. Lewis’s claim that “categories are what obey laws” (1953), we offer a view on which human reasoners have the basic-level categories that they do because of their interactions with the world; basic level categories emerge from learning causal relations that are relevant to a reasoners’ goals, needs, and desires. Specifically, a particular function needs to be expressed at a certain level of inclusion (e.g. transportation on land for going to work). The level of a given function implies the level of its cause (e.g. car) that best predicts the function. The level of this “cause category” is the basic level.
- Ichien, N., Lu, H., & Holyoak, K.J. (2019). Individual differences in judging similarity between semantic relations. Proceedings of the 41st Annual Meeting of the Cognitive Science Society. Montreal, Canada: Cognitive Science Society.
- Peng, Y., Ichien, N., & Lu, H. (2019). Perception of continuous movements from causal actions. Proceedings of the 41st Annual Meeting of the Cognitive Science Society. Montreal, Canada: Cognitive Science Society.
- Lu, H., Liu, Q, Ichien, N., Yuille, A., & Holyoak, K. J. (2019). Seeing the meaning: Vision meet semantics in solving pictorial analogy problems. Proceedings of the 41st Annual Meeting of the Cognitive Science Society. Montreal, Canada: Cognitive Science Society.
- Stamenkovic, D., Ichien, N.T., & Holyoak, K.T. (2019). Metaphor comprehension: An individual differences approach. Journal of Memory and Language, 105, 108-118.
- Telzer, E.H., Ichien, N.T., & Qu, Y. (2015). Mothers know best: Redirecting adolescent reward sensitivity to promote safe behavior during risk taking. Social Cognitive Affective Neuroscience, 10, 1383 -1391. doi: 10.1093/scan/nsv026
- Telzer, E.H., Ichien, N.T., & Qu, Y. (2015). The ties that bind: Group membership shapes the neural correlates of ingroup positivity. NeuroImage, 115, 42-51. doi:10.1016/j.neuroimage.2015.04.035